Credit line dynamic management system and method based on AI large model

Abstract

The invention relates to a credit line dynamic management system and method based on an AI large model, and the system comprises the following modules: a data interface module which is used for collecting the real-time financial data, transaction records, credit records and other related information of an individual or an enterprise; the multi-dimensional data analysis engine inputs the data into an analysis engine based on an AI large model; according to the analysis result, the AI model can automatically generate a personalized credit line and repayment deadline strategy for each borrower; and the market risk prediction model integrates the market risk model and predicts the influence of economic fluctuation on the borrowers. According to the invention, banks and financial institutions can provide more personalized and flexible credit services, the credit safety is ensured, and the service quality and the customer experience are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of credit service, and in particular to a credit limit dynamic management system and method based on an AI big model. Background Art

[0002] As the financial market continues to evolve, individuals and businesses are increasingly demanding credit services, seeking more flexible and personalized financial solutions. However, traditional credit scoring systems, which mainly rely on borrowers' historical credit records and static financial data, often fail to fully reflect borrowers' current financial status and market changes. These limitations cause banks and financial institutions to face challenges in assessing credit risk.

[0003] As the economy fluctuates and interest rates change, borrowers' income, expenditure and debt repayment ability are also constantly changing, and traditional credit scoring models cannot capture these dynamic factors in real time. Therefore, financial institutions urgently need an intelligent system that can analyze borrowers' financial status and market environment in real time and dynamically adjust credit limits to more accurately assess and manage credit risks and meet the market's demand for efficient and flexible credit services. Summary of the invention

[0004] (I) Purpose of the invention

[0005] In order to overcome the above shortcomings, the purpose of the present invention is to provide a credit limit dynamic management system and method based on an AI big model to solve the above technical problems.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the technical solutions provided by this application are as follows:

[0008] A credit limit dynamic management system based on AI big model, including the following modules:

[0009] Data interface module, used to collect real-time financial data, transaction records, credit records and other relevant information of individuals or enterprises;

[0010] Multi-dimensional data analysis engine, which inputs data into the analysis engine based on AI big model, which can process text, numerical, category and other data types, and update user portraits in real time;

[0011] Automatic credit strategy generator: Based on the analysis results, the AI ​​model will automatically generate a personalized credit limit and repayment period strategy for each borrower;

[0012] Market risk prediction model: integrates market risk models to predict the impact of economic fluctuations on borrowers, and feeds the prediction results back to the credit strategy generator to dynamically adjust the strategy;

[0013] The risk control monitoring model monitors the use of credit lines and borrowers' repayments, calculates and adjusts risks in real time, and ensures credit security.

[0014] Preferably, the data interface module specifically includes the following steps:

[0015] A1 Connect to data sources, identify and select legitimate data sources, including bank transaction records, social media, and public credit reports. Establish connections with data sources through API interfaces, database connections, or other data exchange formats, and obtain data regularly or in real time according to business needs to ensure the timeliness of data;

[0016] A2 data pre-extraction, according to preset rules and standards, screens out data fields related to credit assessment, classifies data by type for subsequent processing, and removes obvious invalid data, including null values ​​and outliers.

[0017] A3 data format conversion: convert the collected data into a format that the system can process and store, including JSON, CSV or database table structure, and map the data to a predefined model to ensure the uniformity and compatibility of the data format for subsequent data processing and analysis;

[0018] A4 data cleaning, using data deduplication technology to remove duplicate records to avoid data redundancy and repeated calculations, correct obvious data errors, and handle missing values. Missing values ​​are handled by filling, deleting or interpolating according to specific circumstances;

[0019] A5 feature engineering: Based on the credit assessment model, select features that have a significant impact on the prediction target. Through feature selection and feature engineering, extract key information useful for credit decision-making, including the user's basic information, credit history, income status, and debt status;

[0020] A6 Data conversion and standardization: perform necessary data conversion, including logarithmic conversion, standardization or normalization, to eliminate the dimensional differences between different features;

[0021] A7 Data quality check ensures the logical consistency of the data, checks whether the data is complete, whether the proportion of missing values ​​is within an acceptable range, and verifies the accuracy of the data by comparing with known results or external data sources;

[0022] A8 data storage imports the cleaned data into the database to ensure data consistency and integrity, and regularly backs up the database to prevent data loss or damage. At the same time, security measures are implemented to protect the data from unauthorized access or tampering.

[0023] Preferably, the multi-dimensional data analysis engine specifically includes the following steps:

[0024] B1 data loading: loading cleaned data from the database to provide accurate and standardized input for model training;

[0025] B2 model selection: select the most suitable AI model based on business needs and data characteristics;

[0026] B3 data splitting: randomly split the data into training set and validation set with a ratio of 70:30 or 80:20 to verify the effectiveness of the model during the training process;

[0027] B4 model training: Use the training set data to train the selected AI model and iteratively optimize the model parameters to minimize the prediction error;

[0028] B5 Model evaluation: Use the validation set to conduct comprehensive testing and evaluation on the trained model to determine the accuracy and generalization ability of the model;

[0029] B6 Model optimization: fine-tuning the model based on the evaluation results, including adjusting model parameters, adding or reducing features, or trying different algorithms;

[0030] B7 Model Retraining: After tuning, the model needs to be retrained to ensure that the model achieves optimal performance;

[0031] B8 Model Deployment, deploying validated models into production environments so they can process real data and generate predictions;

[0032] B9 model monitoring and updating: continuously monitor the performance of the model, regularly evaluate its accuracy and efficiency, and make necessary updates to the model based on the latest data or business needs.

[0033] Preferably, the automatic credit strategy generator specifically includes the following steps:

[0034] C1 loads the credit model and loads the pre-trained credit model from the model library to ensure that the model has been optimized for the credit assessment task;

[0035] C2 obtains market risk assessment and obtains real-time market risk data from market risk models, including economic indicators, market trends, policy changes and other relevant information;

[0036] C3 obtains risk control assessment and obtains the current risk assessment from the risk control model, including the borrower's credit risk, repayment ability and historical default record;

[0037] C4 formulates credit strategies, combining credit models, market risk assessments and risk control assessments to generate personalized credit strategies, including credit limits, interest rates and repayment periods;

[0038] C5 internal audit: conduct a detailed internal audit of the generated credit strategy to ensure that the strategy complies with the credit policy and regulatory requirements of the financial institution;

[0039] C6 Strategy Adjustment: Make necessary adjustments to credit strategies based on the results of internal audits to optimize credit conditions and risk control measures;

[0040] C7 Final Approval of Strategy: After the credit strategy has been reviewed and adjusted, it will be finally approved by the relevant person in charge to ensure the rationality and feasibility of the strategy;

[0041] C8 Strategy Verification: Verify the approved credit strategy in the actual credit environment and evaluate its performance and effectiveness in real scenarios;

[0042] C9 strategy deployment deploys proven credit strategies into the credit system so that they can be automatically executed in the daily credit approval process.

[0043] Preferably, the market risk prediction model specifically includes the following steps:

[0044] D1 loads market data, collects and integrates key economic indicators, political stability and financial market data that affect the credit market, and provides basic data for subsequent market trend forecasts and risk assessments;

[0045] D2 market trend forecasting, using statistical analysis and forecasting models to conduct trend analysis on the collected market data and predict the future direction of the market;

[0046] D3 Risk factor identification: Identify and list risk factors that may affect the credit market, including interest rate fluctuations, economic recession and political changes, in preparation for risk quantification;

[0047] D4 Risk Quantification: Quantitative analysis of identified risk factors using statistical models and machine learning algorithms to determine their potential impact on the credit market;

[0048] D5 risk simulation, by simulating different market scenarios, evaluates the performance and possible risk exposure of credit assets in different market environments;

[0049] D6 Risk Score: Based on the results of risk simulation, a risk score is generated for each simulation to quantitatively assess the risk;

[0050] D7 Risk Result Assessment: Combine risk scores with preset risk thresholds to assess potential market risks and determine risk levels and possible impacts;

[0051] D8 publishes risk reports, compiling the results of risk assessment into detailed risk reports, providing decision support for the strategy generator and serving as a basis for internal and external communications;

[0052] D9 Review and Adjustment: Based on the latest market changes and feedback from risk strategy generators, regularly review the effectiveness of the market risk model and make adjustments as needed to ensure the accuracy and adaptability of the model.

[0053] Preferably, the risk control monitoring model specifically includes the following steps:

[0054] E1 loads risk control data, collects and integrates borrower information, account status, historical transactions and other key risk control related data, and provides comprehensive data support for risk assessment and management;

[0055] E2 risk scoring uses the collected data to score borrowers based on preset risk control rules and models to quantify their credit risk;

[0056] E3 risk warning, by analyzing borrowers’ behavior and historical data, identifying potential risk signals, such as loan delinquency and insufficient repayment, so that timely measures can be taken;

[0057] E4: Implement risk mitigation measures. For borrowers identified as high risk, implement pre-defined risk mitigation measures, including adjusting credit limits, increasing collateral requirements, or requiring more financial proof;

[0058] E5 continuous monitoring, continuously monitors the borrower's credit status, tracks market trends and borrower repayment behavior in real time, and ensures that risks are within controllable range;

[0059] E6 updates risk control records, records monitoring results and the implementation of risk mitigation measures in detail, and provides historical data support for future risk assessment and model optimization;

[0060] E7 Analyze the effectiveness of risk control measures and regularly analyze the effectiveness of risk control measures to assess their actual contribution to reducing risks and losses, so as to determine the effectiveness of risk control strategies;

[0061] E8 generates risk control reports, compiling the analysis results, improvement plans and risk management strategies of risk control activities into detailed reports to provide decision support for management and for internal audit and external regulatory reporting.

[0062] A credit limit dynamic management method based on an AI big model comprises the following steps:

[0063] Step 1: Activate the data interface module.

[0064] Start the data interface module and start to establish connections with data sources in banking systems, financial institution databases, and credit rating agencies to collect real-time financial data, transaction records, and credit records of individuals and enterprises;

[0065] Step 2: Data is transferred to the multi-dimensional data analysis engine.

[0066] The collected data is transferred to a multi-dimensional data analysis engine based on the AI ​​big model to process text data, numerical data, and category data, and update the user profile;

[0067] Step 3: Credit strategy is automatically generated.

[0068] The automatic credit strategy generator automatically generates a personalized credit limit and repayment period strategy for each borrower based on the borrower's specific circumstances and real-time analysis results;

[0069] Step 4: Market risk prediction model runs.

[0070] Integrate market risk models, use macroeconomic data, industry trend data and market dynamics data to predict the impact of economic fluctuations on borrowers. Based on the results of the market risk prediction model, the credit strategy automatic generator dynamically adjusts the credit strategy to adapt to changes in market risks;

[0071] Step 5: Risk control monitoring model,

[0072] Continuously monitor credit limits and borrowers' repayment status, calculate and adjust risks in real time, and ensure credit security.

[0073] Preferably, the information in step 1 covers the individual's income and expenditure, savings status, credit card transaction details, and the company's financial statements, sales data, and tax records.

[0074] Preferably, the step 2 specifically includes:

[0075] Text data processing: using natural language processing technology to analyze text data and understand customers’ specific needs and potential risks;

[0076] Numerical data processing, statistical analysis and trend forecasting of numerical data, including income, expenditure, and balance sheet data;

[0077] Category data processing: classify and analyze category data to understand the characteristics and risk preferences of different categories of customers. Category data includes occupation and industry type;

[0078] User profile updates: Through the analysis engine, multi-dimensional data is processed to update user profiles in real time, accurately presenting the customer's credit status, repayment ability and risk level.

[0079] Preferably, the step 3 specifically includes comprehensively considering the borrower's credit status, repayment ability, risk level and current market conditions. For borrowers with good credit, stable income and healthy balance sheet, a higher credit limit and a longer repayment period will be generated, while for borrowers with poor credit records, unstable income and higher risks, a lower credit limit and a shorter repayment period will be generated.

[0080] Beneficial effects:

[0081] 1. Enhance the flexibility and adaptability of credit services: The system dynamically adjusts the credit limit based on the user's real-time economic situation and risk assessment results to provide more flexible and personalized credit services.

[0082] 2. Improve credit security: Identify and mitigate risks in advance by quantifying market risks and customer default risks.

[0083] 3. Improve the cost-effectiveness of lending: Reduce the risk costs of banks and financial institutions through intelligent analysis and risk control models.

[0084] 4. Improve financial efficiency: Automated data processing and credit strategy generation reduce manual operations and significantly improve the efficiency of loan approval and issuance.

[0085] 5. Optimize customer experience: The user interaction interface is intuitive and easy to operate, which improves customer satisfaction.

[0086] By applying the credit limit dynamic management system of the present invention, banks and financial institutions can provide more personalized and flexible credit services while ensuring credit security and improving service quality and customer experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is the overall flow chart of the present invention;

[0088] Figure 2 is a data interface flow chart according to an embodiment of the present invention;

[0089] Figure 3 is a flow chart of a multi-dimensional data analysis engine according to an embodiment of the present invention;

[0090] Figure 4 is a flow chart of a credit strategy automatic generator according to one embodiment of the present invention;

[0091] Figure 5 is a flow chart of a market risk prediction model according to an embodiment of the present invention;

[0092] Figure 6 It is a flow chart of a risk control monitoring model according to an implementation mode of the present invention. DETAILED DESCRIPTION

[0093] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following is a detailed description of the present invention in conjunction with the specific implementation methods and with reference to the attached drawings. Figure 1-6 , the present invention is further described in detail. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0094] The present invention provides a credit limit dynamic management system based on an AI big model, comprising the following modules:

[0095] Data interface module, used to collect real-time financial data, transaction records, credit records and other relevant information of individuals or enterprises;

[0096] Multi-dimensional data analysis engine, which inputs data into the analysis engine based on AI big model, which can process text, numerical, category and other data types, and update user portraits in real time;

[0097] Automatic credit strategy generator: Based on the analysis results, the AI ​​model will automatically generate a personalized credit limit and repayment period strategy for each borrower;

[0098] Market risk prediction model: integrates market risk models to predict the impact of economic fluctuations on borrowers, and feeds the prediction results back to the credit strategy generator to dynamically adjust the strategy;

[0099] The risk control monitoring model monitors the use of credit lines and borrowers' repayments, calculates and adjusts risks in real time, and ensures credit security.

[0100] The data interface module specifically includes the following steps:

[0101] A1 Connect to data sources, identify and select legitimate data sources, including bank transaction records, social media, and public credit reports. Establish connections with data sources through API interfaces, database connections, or other data exchange formats, and obtain data regularly or in real time according to business needs to ensure the timeliness of data;

[0102] A2 data pre-extraction, according to preset rules and standards, screens out data fields related to credit assessment, classifies data by type for subsequent processing, and removes obvious invalid data, including null values ​​and outliers.

[0103] A3 data format conversion: convert the collected data into a format that the system can process and store, including JSON, CSV or database table structure, and map the data to a predefined model to ensure the uniformity and compatibility of the data format for subsequent data processing and analysis;

[0104] A4 data cleaning, using data deduplication technology to remove duplicate records to avoid data redundancy and repeated calculations, correct obvious data errors, and handle missing values. Missing values ​​are handled by filling, deleting or interpolating according to specific circumstances;

[0105] A5 feature engineering: Based on the credit assessment model, select features that have a significant impact on the prediction target. Through feature selection and feature engineering, extract key information useful for credit decision-making, including the user's basic information, credit history, income status, and debt status;

[0106] A6 Data conversion and standardization: perform necessary data conversion, including logarithmic conversion, standardization or normalization, to eliminate the dimensional differences between different features;

[0107] A7 Data quality check ensures the logical consistency of the data, checks whether the data is complete, whether the proportion of missing values ​​is within an acceptable range, and verifies the accuracy of the data by comparing with known results or external data sources;

[0108] A8 data storage imports the cleaned data into the database to ensure data consistency and integrity, and regularly backs up the database to prevent data loss or damage. At the same time, security measures are implemented to protect the data from unauthorized access or tampering.

[0109] The multi-dimensional data analysis engine specifically includes the following steps:

[0110] B1 data loading: loading cleaned data from the database to provide accurate and standardized input for model training;

[0111] B2 model selection: select the most suitable AI model based on business needs and data characteristics;

[0112] B3 data splitting: randomly split the data into training set and validation set with a ratio of 70:30 or 80:20 to verify the effectiveness of the model during the training process;

[0113] B4 model training: Use the training set data to train the selected AI model and iteratively optimize the model parameters to minimize the prediction error;

[0114] B5 Model evaluation: Use the validation set to conduct comprehensive testing and evaluation on the trained model to determine the accuracy and generalization ability of the model;

[0115] B6 Model optimization: fine-tuning the model based on the evaluation results, including adjusting model parameters, adding or reducing features, or trying different algorithms;

[0116] B7 Model Retraining: After tuning, the model needs to be retrained to ensure that the model achieves optimal performance;

[0117] B8 Model Deployment, deploying validated models into production environments so they can process real data and generate predictions;

[0118] B9 model monitoring and updating: continuously monitor the performance of the model, regularly evaluate its accuracy and efficiency, and make necessary updates to the model based on the latest data or business needs.

[0119] The automatic credit strategy generator specifically includes the following steps:

[0120] C1 loads the credit model and loads the pre-trained credit model from the model library to ensure that the model has been optimized for the credit assessment task;

[0121] C2 obtains market risk assessment and obtains real-time market risk data from market risk models, including economic indicators, market trends, policy changes and other relevant information;

[0122] C3 obtains risk control assessment and obtains the current risk assessment from the risk control model, including the borrower's credit risk, repayment ability and historical default record;

[0123] C4 formulates credit strategies, combining credit models, market risk assessments and risk control assessments to generate personalized credit strategies, including credit limits, interest rates and repayment periods;

[0124] C5 internal audit: conduct a detailed internal audit of the generated credit strategy to ensure that the strategy complies with the credit policy and regulatory requirements of the financial institution;

[0125] C6 Strategy Adjustment: Make necessary adjustments to credit strategies based on the results of internal audits to optimize credit conditions and risk control measures;

[0126] C7 Final Approval of Strategy: After the credit strategy has been reviewed and adjusted, it will be finally approved by the relevant person in charge to ensure the rationality and feasibility of the strategy;

[0127] C8 Strategy Verification: Verify the approved credit strategy in the actual credit environment and evaluate its performance and effectiveness in real scenarios;

[0128] C9 strategy deployment deploys proven credit strategies into the credit system so that they can be automatically executed in the daily credit approval process.

[0129] The market risk prediction model specifically includes the following steps:

[0130] D1 loads market data, collects and integrates key economic indicators, political stability and financial market data that affect the credit market, and provides basic data for subsequent market trend forecasts and risk assessments;

[0131] D2 market trend forecasting, using statistical analysis and forecasting models to conduct trend analysis on the collected market data and predict the future direction of the market;

[0132] D3 Risk factor identification: Identify and list risk factors that may affect the credit market, including interest rate fluctuations, economic recession and political changes, in preparation for risk quantification;

[0133] D4 Risk Quantification: Quantitative analysis of identified risk factors using statistical models and machine learning algorithms to determine their potential impact on the credit market;

[0134] D5 risk simulation, by simulating different market scenarios, evaluates the performance and possible risk exposure of credit assets in different market environments;

[0135] D6 Risk Score: Based on the results of risk simulation, a risk score is generated for each simulation to quantitatively assess the risk;

[0136] D7 Risk Result Assessment: Combine risk scores with preset risk thresholds to assess potential market risks and determine risk levels and possible impacts;

[0137] D8 publishes risk reports, compiling the results of risk assessment into detailed risk reports, providing decision support for the strategy generator and serving as a basis for internal and external communications;

[0138] D9 Review and Adjustment: Based on the latest market changes and feedback from risk strategy generators, regularly review the effectiveness of the market risk model and make adjustments as needed to ensure the accuracy and adaptability of the model.

[0139] The risk control monitoring model specifically includes the following steps:

[0140] E1 loads risk control data, collects and integrates borrower information, account status, historical transactions and other key risk control related data, and provides comprehensive data support for risk assessment and management;

[0141] E2 risk scoring uses the collected data to score borrowers based on preset risk control rules and models to quantify their credit risk;

[0142] E3 risk warning, by analyzing borrowers’ behavior and historical data, identifying potential risk signals, such as loan delinquency and insufficient repayment, so that timely measures can be taken;

[0143] E4: Implement risk mitigation measures. For borrowers identified as high risk, implement pre-defined risk mitigation measures, including adjusting credit limits, increasing collateral requirements, or requiring more financial proof;

[0144] E5 continuous monitoring, continuously monitors the borrower's credit status, tracks market trends and borrower repayment behavior in real time, and ensures that risks are within controllable range;

[0145] E6 updates risk control records, records monitoring results and the implementation of risk mitigation measures in detail, and provides historical data support for future risk assessment and model optimization;

[0146] E7 Analyze the effectiveness of risk control measures and regularly analyze the effectiveness of risk control measures to assess their actual contribution to reducing risks and losses, so as to determine the effectiveness of risk control strategies;

[0147] E8 generates risk control reports, compiling the analysis results, improvement plans and risk management strategies of risk control activities into detailed reports to provide decision support for management and for internal audit and external regulatory reporting.

[0148] A credit limit dynamic management method based on an AI big model, characterized by comprising the following steps:

[0149] Step 1: Activate the data interface module.

[0150] Start the data interface module and start to establish connections with data sources in banking systems, financial institution databases, and credit rating agencies to collect real-time financial data, transaction records, and credit records of individuals and enterprises. The information covers personal income and expenditure, savings status, credit card transaction details, and corporate financial statements, sales data, and tax records.

[0151] Step 2: Data is transferred to the multi-dimensional data analysis engine.

[0152] The collected data is transferred to a multi-dimensional data analysis engine based on the AI ​​big model to process text data, numerical data, and category data, and update the user profile;

[0153] Text data processing: using natural language processing technology to analyze text data and understand customers’ specific needs and potential risks;

[0154] Numerical data processing, statistical analysis and trend forecasting of numerical data, including income, expenditure, and balance sheet data;

[0155] Category data processing: classify and analyze category data to understand the characteristics and risk preferences of different categories of customers. Category data includes occupation and industry type;

[0156] User profile updates: Through the analysis engine, multi-dimensional data is processed to update user profiles in real time, accurately presenting the customer's credit status, repayment ability and risk level.

[0157] Step 3: Credit strategy is automatically generated.

[0158] The automatic credit strategy generator automatically generates a personalized credit limit and repayment period strategy for each borrower based on the borrower's specific situation and real-time analysis results, taking into account the borrower's credit status, repayment ability, risk level and current market conditions. For borrowers with good credit, stable income and healthy balance sheet, a higher credit limit and a longer repayment period will be generated, while for borrowers with poor credit records, unstable income and high risk, a lower credit limit and a shorter repayment period will be generated.

[0159] Step 4: Market risk prediction model runs.

[0160] The integrated market risk model uses macroeconomic data, industry trend data, and market dynamics data to predict the impact of economic fluctuations on borrowers, predict the impact of economic fluctuations on borrowers, and predict the impact of interest rate changes, inflation, and economic recession on borrowers' repayment ability. Based on the results of the market risk prediction model, the credit strategy automatic generator dynamically adjusts the credit strategy to adapt to changes in market risks. When predicting an impending economic recession, the credit strategy automatic generator may reduce the credit limit and shorten the repayment period to reduce the risk of financial institutions; when predicting strong economic growth, it may increase the credit limit and extend the repayment period to meet the needs of borrowers and promote business growth;

[0161] Step 5: Risk control monitoring model,

[0162] Continuously monitor the credit limit and the repayment status of the borrower, calculate and adjust the risk in real time, and ensure credit security. Monitor whether the borrower's use of the loan is in accordance with the agreed purpose, whether the repayment is on time, and whether the repayment amount meets the requirements. Once a risk signal is found in the borrower, such as overdue repayment and improper use of the loan, the risk control monitoring model will immediately issue an alarm and take corresponding measures, including reminding the borrower to repay, adjusting the credit limit and strengthening collection. At the same time, the risk control monitoring model will also feed back this risk information to the multi-dimensional data analysis engine and the credit strategy automatic generator, prompting them to further adjust and optimize the user profile and credit strategy.

[0163] The present invention has the following beneficial effects:

[0164] 1. Enhance the flexibility and adaptability of credit services: The system dynamically adjusts the credit limit based on the user's real-time economic situation and risk assessment results to provide more flexible and personalized credit services.

[0165] 2. Improve credit security: Identify and mitigate risks in advance by quantifying market risks and customer default risks.

[0166] 3. Improve the cost-effectiveness of lending: Reduce the risk costs of banks and financial institutions through intelligent analysis and risk control models.

[0167] 4. Improve financial efficiency: Automated data processing and credit strategy generation reduce manual operations and significantly improve the efficiency of loan approval and issuance.

[0168] 5. Optimize customer experience: The user interaction interface is intuitive and easy to operate, which improves customer satisfaction.

[0169] By applying the credit limit dynamic management system of the present invention, banks and financial institutions can provide more personalized and flexible credit services while ensuring credit security and improving service quality and customer experience.

[0170] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0171] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A credit limit dynamic management system based on AI big model, characterized in that: Includes the following modules: Data interface module, used to collect real-time financial data, transaction records, credit records and other relevant information of individuals or enterprises; Multi-dimensional data analysis engine, which inputs data into an analysis engine based on a large AI model. The engine can process text, numerical values, categories and other data types, and update user portraits in real time; Automatic credit strategy generator: Based on the analysis results, the AI ​​model will automatically generate a personalized credit limit and repayment period strategy for each borrower; Market risk prediction model: integrates market risk models to predict the impact of economic fluctuations on borrowers, and feeds the prediction results back to the credit strategy generator to dynamically adjust the strategy; The risk control monitoring model monitors the use of credit lines and borrowers' repayments, calculates and adjusts risks in real time, and ensures credit security.

2. According to claim 1, a credit limit dynamic management system based on AI big model is characterized in that: The data interface module specifically includes the following steps: A1 Connect to data sources, identify and select legitimate data sources, including bank transaction records, social media, and public credit reports. Establish connections with data sources through API interfaces, database connections, or other data exchange formats, and obtain data regularly or in real time according to business needs to ensure the timeliness of data; A2 data pre-extraction, according to preset rules and standards, screens out data fields related to credit assessment, classifies data by type for subsequent processing, and removes obvious invalid data, including null values ​​and outliers. A3 data format conversion: convert the collected data into a format that the system can process and store, including JSON, CSV or database table structure, and map the data to a predefined model to ensure the uniformity and compatibility of the data format for subsequent data processing and analysis; A4 data cleaning, using data deduplication technology to remove duplicate records to avoid data redundancy and repeated calculations, correct obvious data errors, and handle missing values. Missing values ​​are handled by filling, deleting or interpolating according to specific circumstances; A5 feature engineering: Based on the credit assessment model, select features that have a significant impact on the prediction target. Through feature selection and feature engineering, extract key information useful for credit decision-making, including the user's basic information, credit history, income status, and debt status; A6 Data conversion and standardization: perform necessary data conversion, including logarithmic conversion, standardization or normalization, to eliminate the dimensional differences between different features; A7 Data quality check ensures the logical consistency of the data, checks whether the data is complete, whether the proportion of missing values ​​is within an acceptable range, and verifies the accuracy of the data by comparing with known results or external data sources; A8 data storage imports the cleaned data into the database to ensure data consistency and integrity, and regularly backs up the database to prevent data loss or damage. At the same time, security measures are implemented to protect the data from unauthorized access or tampering.

3. According to claim 1, a credit limit dynamic management system based on AI big model is characterized in that: The multi-dimensional data analysis engine specifically includes the following steps: B1 data loading: loading cleaned data from the database to provide accurate and standardized input for model training; B2 model selection: select the most suitable AI model based on business needs and data characteristics; B3 data splitting: randomly split the data into training set and validation set with a ratio of 70:30 or 80:20 to verify the effectiveness of the model during the training process; B4 model training: Use the training set data to train the selected AI model and iteratively optimize the model parameters to minimize the prediction error; B5 Model evaluation: Use the validation set to conduct comprehensive testing and evaluation on the trained model to determine the accuracy and generalization ability of the model; B6 Model optimization: fine-tuning the model based on the evaluation results, including adjusting model parameters, adding or reducing features, or trying different algorithms; B7 Model Retraining: After tuning, the model needs to be retrained to ensure that the model achieves optimal performance; B8 Model Deployment, deploying validated models into production environments so they can process real data and generate predictions; B9 model monitoring and updating: continuously monitor the performance of the model, regularly evaluate its accuracy and efficiency, and make necessary updates to the model based on the latest data or business needs.

4. According to claim 1, a credit limit dynamic management system based on AI big model is characterized in that: The automatic credit strategy generator specifically includes the following steps: C1 loads the credit model and loads the pre-trained credit model from the model library to ensure that the model has been optimized for the credit assessment task; C2 obtains market risk assessment and obtains real-time market risk data from market risk models, including economic indicators, market trends, policy changes and other relevant information; C3 obtains risk control assessment and obtains the current risk assessment from the risk control model, including the borrower's credit risk, repayment ability and historical default record; C4 formulates credit strategies, combining credit models, market risk assessments and risk control assessments to generate personalized credit strategies, including credit limits, interest rates and repayment periods; C5 internal audit: conduct a detailed internal audit of the generated credit strategy to ensure that the strategy complies with the credit policy and regulatory requirements of the financial institution; C6 Strategy Adjustment: Make necessary adjustments to credit strategies based on the results of internal audits to optimize credit conditions and risk control measures; C7 Final Approval of Strategy: After the credit strategy has been reviewed and adjusted, it will be finally approved by the relevant person in charge to ensure the rationality and feasibility of the strategy; C8 Strategy Verification: Verify the approved credit strategy in the actual credit environment and evaluate its performance and effectiveness in real scenarios; C9 strategy deployment deploys proven credit strategies into the credit system so that they can be automatically executed in the daily credit approval process.

5. According to claim 1, a credit limit dynamic management system based on AI big model is characterized in that: The market risk prediction model specifically includes the following steps: D1 loads market data, collects and integrates key economic indicators, political stability and financial market data that affect the credit market, and provides basic data for subsequent market trend forecasts and risk assessments; D2 market trend forecasting, using statistical analysis and forecasting models to conduct trend analysis on the collected market data and predict the future direction of the market; D3 Risk factor identification: Identify and list risk factors that may affect the credit market, including interest rate fluctuations, economic recession and political changes, in preparation for risk quantification; D4 Risk Quantification: Quantitative analysis of identified risk factors using statistical models and machine learning algorithms to determine their potential impact on the credit market; D5 risk simulation, by simulating different market scenarios, evaluates the performance and possible risk exposure of credit assets in different market environments; D6 Risk Score: Based on the results of risk simulation, a risk score is generated for each simulation to quantitatively assess the risk; D7 Risk Result Assessment: Combine risk scores with preset risk thresholds to assess potential market risks and determine risk levels and possible impacts; D8 publishes risk reports, compiling the results of risk assessment into detailed risk reports, providing decision support for the strategy generator and serving as a basis for internal and external communications; D9 Review and Adjustment: Based on the latest market changes and feedback from risk strategy generators, regularly review the effectiveness of the market risk model and make adjustments as needed to ensure the accuracy and adaptability of the model.

6. According to claim 1, a credit limit dynamic management system based on AI big model is characterized in that: The risk control monitoring model specifically includes the following steps: E1 loads risk control data, collects and integrates borrower information, account status, historical transactions and other key risk control related data, and provides comprehensive data support for risk assessment and management; E2 risk scoring uses the collected data to score borrowers based on preset risk control rules and models to quantify their credit risk; E3 risk warning, by analyzing borrowers’ behavior and historical data, identifying potential risk signals, such as loan delinquency and insufficient repayment, so that timely measures can be taken; E4: Implement risk mitigation measures. For borrowers identified as high risk, implement pre-defined risk mitigation measures, including adjusting credit limits, increasing collateral requirements, or requiring more financial proof; E5 continuous monitoring, continuously monitors the borrower's credit status, tracks market trends and borrower repayment behavior in real time, and ensures that risks are within controllable range; E6 updates risk control records, records monitoring results and the implementation of risk mitigation measures in detail, and provides historical data support for future risk assessment and model optimization; E7 Analyze the effectiveness of risk control measures and regularly analyze the effectiveness of risk control measures to assess their actual contribution to reducing risks and losses, so as to determine the effectiveness of risk control strategies; E8 generates risk control reports, compiling the analysis results, improvement plans and risk management strategies of risk control activities into detailed reports to provide decision support for management and for internal audit and external regulatory reporting.

7. A credit limit dynamic management method based on AI big model, characterized in that: The following steps are involved: Step 1: Activate the data interface module. Start the data interface module and start to establish connections with data sources in banking systems, financial institution databases, and credit rating agencies to collect real-time financial data, transaction records, and credit records of individuals and enterprises; Step 2: Data is transferred to the multi-dimensional data analysis engine. The collected data is transferred to a multi-dimensional data analysis engine based on the AI ​​big model to process text data, numerical data, and category data, and update the user profile; Step 3: Credit strategy is automatically generated. The automatic credit strategy generator automatically generates a personalized credit limit and repayment period strategy for each borrower based on the borrower's specific circumstances and real-time analysis results; Step 4: Market risk prediction model runs. Integrate market risk models, use macroeconomic data, industry trend data and market dynamics data to predict the impact of economic fluctuations on borrowers. Based on the results of the market risk prediction model, the credit strategy automatic generator dynamically adjusts the credit strategy to adapt to changes in market risks; Step 5: Risk control monitoring model, Continuously monitor credit limits and borrowers' repayment status, calculate and adjust risks in real time, and ensure credit security.

8. The method for dynamic credit line management based on AI big model according to claim 7 is characterized in that: The information in step 1 covers personal income and expenditure, savings status, credit card transaction details, and business financial statements, sales data, and tax records.

9. The method for dynamic credit line management based on AI big model according to claim 7 is characterized in that: The step 2 specifically includes: Text data processing: using natural language processing technology to analyze text data and understand customers’ specific needs and potential risks; Numerical data processing, statistical analysis and trend forecasting of numerical data, including income, expenditure, and balance sheet data; Category data processing: classify and analyze category data to understand the characteristics and risk preferences of different categories of customers. Category data includes occupation and industry type; User profile updates: Through the analysis engine, multi-dimensional data is processed to update user profiles in real time, accurately presenting the customer's credit status, repayment ability and risk level.

10. The method for dynamic credit line management based on AI big model according to claim 7, characterized in that: The step 3 specifically includes comprehensively considering the borrower's credit status, repayment ability, risk level and current market conditions. For borrowers with good credit, stable income and healthy balance sheet, a higher credit limit and a longer repayment period will be generated, while for borrowers with poor credit records, unstable income and higher risks, a lower credit limit and a shorter repayment period will be generated.

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